Overview

Dataset statistics

Number of variables21
Number of observations1175
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory617.4 KiB
Average record size in memory538.0 B

Variable types

NUM15
CAT5
UNSUPPORTED1

Reproduction

Analysis started2021-03-27 17:01:53.098624
Analysis finished2021-03-27 17:02:24.299713
Duration31.2 seconds
Versionpandas-profiling v2.7.1
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml
title has a high cardinality: 1168 distinct values High cardinality
deltaMedianPrice is highly correlated with deltaAvgPriceHigh correlation
deltaAvgPrice is highly correlated with deltaMedianPriceHigh correlation
dublinNorthSouth is highly correlated with neighbourhoodHigh correlation
neighbourhood is highly correlated with dublinNorthSouthHigh correlation
title is uniformly distributed Uniform
df_index has unique values Unique
floorArea is an unsupported type, check if it needs cleaning or further analysis Unsupported
deltaMedianPrice has 51 (4.3%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

UNIQUE
Distinct count1175
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1384.9131914893617
Minimum0
Maximum2818
Zeros1
Zeros (%)0.1%
Memory size9.3 KiB
2021-03-27T17:02:24.367670image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile178.7
Q1761
median1385
Q31998.5
95-th percentile2594.3
Maximum2818
Range2818
Interquartile range (IQR)1237.5

Descriptive statistics

Standard deviation757.2597258
Coefficient of variation (CV)0.5467921964
Kurtosis-1.05217277
Mean1384.913191
Median Absolute Deviation (MAD)620
Skewness0.003204653992
Sum1627273
Variance573442.2923
2021-03-27T17:02:24.463218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 1 0.1%
 
1306 1 0.1%
 
1341 1 0.1%
 
1340 1 0.1%
 
1338 1 0.1%
 
2391 1 0.1%
 
1335 1 0.1%
 
1333 1 0.1%
 
1330 1 0.1%
 
2356 1 0.1%
 
Other values (1165) 1165 99.1%
 
ValueCountFrequency (%) 
0 1 0.1%
 
2 1 0.1%
 
4 1 0.1%
 
12 1 0.1%
 
13 1 0.1%
 
ValueCountFrequency (%) 
2818 1 0.1%
 
2817 1 0.1%
 
2815 1 0.1%
 
2809 1 0.1%
 
2800 1 0.1%
 

title
Categorical

HIGH CARDINALITY
UNIFORM
Distinct count1168
Unique (%)99.4%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
Apartment 406, Beacon One, Sandyford, Dublin 18
 
2
171 Drimnagh Road, Drimnagh, Dublin 12
 
2
20 Moyclare Road, Baldoyle, Dublin 13
 
2
24 The Wood, Millbrook Lawns, Tallaght, Dublin 24
 
2
20 Muckross Green, Perrystown, Dublin 12
 
2
Other values (1163)
1165
ValueCountFrequency (%) 
Apartment 406, Beacon One, Sandyford, Dublin 18 2 0.2%
 
171 Drimnagh Road, Drimnagh, Dublin 12 2 0.2%
 
20 Moyclare Road, Baldoyle, Dublin 13 2 0.2%
 
24 The Wood, Millbrook Lawns, Tallaght, Dublin 24 2 0.2%
 
20 Muckross Green, Perrystown, Dublin 12 2 0.2%
 
51 Grange Park, Rathfarnham, Dublin 14 2 0.2%
 
1 Fitzgibbon Lane, Dublin 1 2 0.2%
 
37 Saint Anne's Avenue, Raheny, Dublin 5 1 0.1%
 
Apartment 127, Rockview, Blackglen Road, Sandyford, Dublin 18 1 0.1%
 
52 Kildonan Avenue, Finglas West, Finglas, Dublin 11 1 0.1%
 
Other values (1158) 1158 98.6%
 
2021-03-27T17:02:24.575702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length84
Mean length45.15829787
Min length22
ValueCountFrequency (%) 
Lowercase_Letter 26 36.6%
 
Uppercase_Letter 25 35.2%
 
Decimal_Number 10 14.1%
 
Other_Punctuation 5 7.0%
 
Dash_Punctuation 1 1.4%
 
Space_Separator 1 1.4%
 
Math_Symbol 1 1.4%
 
Open_Punctuation 1 1.4%
 
Close_Punctuation 1 1.4%
 
ValueCountFrequency (%) 
Latin 51 71.8%
 
Common 20 28.2%
 
ValueCountFrequency (%) 
ASCII 71 100.0%
 

neighbourhood
Categorical

HIGH CORRELATION
Distinct count22
Unique (%)1.9%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
Dublin 15
 
132
Dublin 11
 
92
Dublin 9
 
73
Dublin 8
 
71
Dublin 3
 
71
Other values (17)
736
ValueCountFrequency (%) 
Dublin 15 132 11.2%
 
Dublin 11 92 7.8%
 
Dublin 9 73 6.2%
 
Dublin 8 71 6.0%
 
Dublin 3 71 6.0%
 
Dublin 7 69 5.9%
 
Dublin 14 66 5.6%
 
Dublin 4 65 5.5%
 
Dublin 24 63 5.4%
 
Dublin 6 61 5.2%
 
Other values (12) 412 35.1%
 
2021-03-27T17:02:24.689906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length9
Mean length8.561702128
Min length8
ValueCountFrequency (%) 
Decimal_Number 10 55.6%
 
Lowercase_Letter 5 27.8%
 
Uppercase_Letter 2 11.1%
 
Space_Separator 1 5.6%
 
ValueCountFrequency (%) 
Common 11 61.1%
 
Latin 7 38.9%
 
ValueCountFrequency (%) 
ASCII 18 100.0%
 

propertyType
Categorical

Distinct count9
Unique (%)0.8%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
Apartment
368
Terrace
313
Semi-D
236
End of Terrace
121
Detached
 
60
Other values (4)
77
ValueCountFrequency (%) 
Apartment 368 31.3%
 
Terrace 313 26.6%
 
Semi-D 236 20.1%
 
End of Terrace 121 10.3%
 
Detached 60 5.1%
 
Duplex 35 3.0%
 
Bungalow 15 1.3%
 
Townhouse 14 1.2%
 
Site 13 1.1%
 
2021-03-27T17:02:24.804154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length14
Mean length8.17106383
Min length4
ValueCountFrequency (%) 
Lowercase_Letter 19 70.4%
 
Uppercase_Letter 6 22.2%
 
Dash_Punctuation 1 3.7%
 
Space_Separator 1 3.7%
 
ValueCountFrequency (%) 
Latin 25 92.6%
 
Common 2 7.4%
 
ValueCountFrequency (%) 
ASCII 27 100.0%
 

numBedrooms
Real number (ℝ)

Distinct count9
Unique (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6
Minimum-1.0
Maximum11.0
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:24.898862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum11
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.008481916
Coefficient of variation (CV)0.38787766
Kurtosis5.690639839
Mean2.6
Median Absolute Deviation (MAD)1
Skewness0.4592048756
Sum3055
Variance1.017035775
2021-03-27T17:02:24.979866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3 488 41.5%
 
2 423 36.0%
 
4 119 10.1%
 
1 98 8.3%
 
5 27 2.3%
 
-1 13 1.1%
 
6 4 0.3%
 
7 2 0.2%
 
11 1 0.1%
 
ValueCountFrequency (%) 
-1 13 1.1%
 
1 98 8.3%
 
2 423 36.0%
 
3 488 41.5%
 
4 119 10.1%
 
ValueCountFrequency (%) 
11 1 0.1%
 
7 2 0.2%
 
6 4 0.3%
 
5 27 2.3%
 
4 119 10.1%
 

numBathrooms
Real number (ℝ)

Distinct count7
Unique (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.643404255319149
Minimum-1.0
Maximum6.0
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:25.064475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum6
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.866559501
Coefficient of variation (CV)0.5272953981
Kurtosis1.42375293
Mean1.643404255
Median Absolute Deviation (MAD)1
Skewness0.5840058131
Sum1931
Variance0.7509253688
2021-03-27T17:02:25.400373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 583 49.6%
 
2 395 33.6%
 
3 149 12.7%
 
4 28 2.4%
 
-1 17 1.4%
 
5 2 0.2%
 
6 1 0.1%
 
ValueCountFrequency (%) 
-1 17 1.4%
 
1 583 49.6%
 
2 395 33.6%
 
3 149 12.7%
 
4 28 2.4%
 
ValueCountFrequency (%) 
6 1 0.1%
 
5 2 0.2%
 
4 28 2.4%
 
3 149 12.7%
 
2 395 33.6%
 

floorArea
Unsupported

REJECTED
UNSUPPORTED
Missing0
Missing (%)0.0%
Memory size9.3 KiB

price
Real number (ℝ≥0)

Distinct count179
Unique (%)15.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean389606.8085106383
Minimum75000.0
Maximum2500000.0
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:25.494758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile195000
Q1260000
median335000
Q3449000
95-th percentile784500
Maximum2500000
Range2425000
Interquartile range (IQR)189000

Descriptive statistics

Standard deviation221596.5077
Coefficient of variation (CV)0.5687695976
Kurtosis23.12062082
Mean389606.8085
Median Absolute Deviation (MAD)85000
Skewness3.634528448
Sum457788000
Variance4.910501222e+10
2021-03-27T17:02:25.577560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
275000 43 3.7%
 
395000 35 3.0%
 
375000 35 3.0%
 
350000 33 2.8%
 
295000 31 2.6%
 
225000 29 2.5%
 
250000 28 2.4%
 
425000 26 2.2%
 
450000 26 2.2%
 
285000 24 2.0%
 
Other values (169) 865 73.6%
 
ValueCountFrequency (%) 
75000 1 0.1%
 
129000 1 0.1%
 
135000 1 0.1%
 
139000 1 0.1%
 
140000 3 0.3%
 
ValueCountFrequency (%) 
2500000 2 0.2%
 
2300000 1 0.1%
 
1800000 1 0.1%
 
1700000 1 0.1%
 
1600000 1 0.1%
 

rating
Categorical

Distinct count16
Unique (%)1.4%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
D2
152
D1
138
C3
118
C2
114
C1
 
98
Other values (11)
555
ValueCountFrequency (%) 
D2 152 12.9%
 
D1 138 11.7%
 
C3 118 10.0%
 
C2 114 9.7%
 
C1 98 8.3%
 
B3 91 7.7%
 
E1 91 7.7%
 
E2 81 6.9%
 
F 70 6.0%
 
G 70 6.0%
 
Other values (6) 152 12.9%
 
2021-03-27T17:02:25.681496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length6
Mean length2.060425532
Min length1
ValueCountFrequency (%) 
Uppercase_Letter 10 66.7%
 
Decimal_Number 4 26.7%
 
Connector_Punctuation 1 6.7%
 
ValueCountFrequency (%) 
Latin 10 66.7%
 
Common 5 33.3%
 
ValueCountFrequency (%) 
ASCII 15 100.0%
 

sellerId
Real number (ℝ≥0)

Distinct count195
Unique (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5787.916595744681
Minimum7
Maximum12149
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:25.768072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q11367
median6996
Q39917
95-th percentile11062
Maximum12149
Range12142
Interquartile range (IQR)8550

Descriptive statistics

Standard deviation4274.074934
Coefficient of variation (CV)0.7384479136
Kurtosis-1.663292854
Mean5787.916596
Median Absolute Deviation (MAD)3953
Skewness-0.1003230644
Sum6800802
Variance18267716.54
2021-03-27T17:02:25.851789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
11062 48 4.1%
 
10947 48 4.1%
 
7569 40 3.4%
 
11 37 3.1%
 
12 29 2.5%
 
1590 27 2.3%
 
3658 26 2.2%
 
1413 24 2.0%
 
10948 24 2.0%
 
9172 23 2.0%
 
Other values (185) 849 72.3%
 
ValueCountFrequency (%) 
7 5 0.4%
 
11 37 3.1%
 
12 29 2.5%
 
49 15 1.3%
 
56 6 0.5%
 
ValueCountFrequency (%) 
12149 1 0.1%
 
12120 3 0.3%
 
11902 6 0.5%
 
11766 4 0.3%
 
11754 1 0.1%
 

longitude
Real number (ℝ)

Distinct count1151
Unique (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.283536942141653
Minimum-6.441123
Maximum-6.055211
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:25.940985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.441123
5-th percentile-6.405139
Q1-6.327493
median-6.279775
Q3-6.2362895
95-th percentile-6.1681232
Maximum-6.055211
Range0.385912
Interquartile range (IQR)0.0912035

Descriptive statistics

Standard deviation0.07180332691
Coefficient of variation (CV)-0.01142721489
Kurtosis-0.1693690409
Mean-6.283536942
Median Absolute Deviation (MAD)0.044036
Skewness-0.009598664888
Sum-7383.155907
Variance0.005155717755
2021-03-27T17:02:26.026931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
-6.371453 3 0.3%
 
-6.34301 2 0.2%
 
-6.309374 2 0.2%
 
-6.146247 2 0.2%
 
-6.25762 2 0.2%
 
-6.267336 2 0.2%
 
-6.355033 2 0.2%
 
-6.256604 2 0.2%
 
-6.374559 2 0.2%
 
-6.248074 2 0.2%
 
Other values (1141) 1154 98.2%
 
ValueCountFrequency (%) 
-6.441123 1 0.1%
 
-6.440657 1 0.1%
 
-6.440503 1 0.1%
 
-6.440373 1 0.1%
 
-6.439065 1 0.1%
 
ValueCountFrequency (%) 
-6.055211 1 0.1%
 
-6.059735 1 0.1%
 
-6.060574 1 0.1%
 
-6.065742 1 0.1%
 
-6.066756 1 0.1%
 

latitude
Real number (ℝ≥0)

Distinct count1146
Unique (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.34522645717897
Minimum53.21904
Maximum53.432174
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:26.122569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum53.21904
5-th percentile53.270019
Q153.3184445
median53.347615
Q353.381695
95-th percentile53.4029602
Maximum53.432174
Range0.213134
Interquartile range (IQR)0.0632505

Descriptive statistics

Standard deviation0.04275896152
Coefficient of variation (CV)0.0008015517856
Kurtosis-0.6508129507
Mean53.34522646
Median Absolute Deviation (MAD)0.032662
Skewness-0.4262771467
Sum62680.64109
Variance0.00182832879
2021-03-27T17:02:26.196786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
53.3188 3 0.3%
 
53.270541 2 0.2%
 
53.357853 2 0.2%
 
53.339611 2 0.2%
 
53.348851 2 0.2%
 
53.31296 2 0.2%
 
53.276203 2 0.2%
 
53.386451 2 0.2%
 
53.341431 2 0.2%
 
53.338922 2 0.2%
 
Other values (1136) 1154 98.2%
 
ValueCountFrequency (%) 
53.21904 1 0.1%
 
53.226358 1 0.1%
 
53.229695 1 0.1%
 
53.231998 1 0.1%
 
53.233244 1 0.1%
 
ValueCountFrequency (%) 
53.432174 1 0.1%
 
53.431643 1 0.1%
 
53.422947 1 0.1%
 
53.422384 1 0.1%
 
53.422316 1 0.1%
 

pricePerBedroom
Real number (ℝ)

Distinct count277
Unique (%)23.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145701.874366768
Minimum-1500000.0
Maximum625000.0
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:26.277165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1500000
5-th percentile76666.66667
Q1107142.8571
median137500
Q3185000
95-th percentile275000
Maximum625000
Range2125000
Interquartile range (IQR)77857.14286

Descriptive statistics

Standard deviation105117.7109
Coefficient of variation (CV)0.7214575058
Kurtosis75.93107687
Mean145701.8744
Median Absolute Deviation (MAD)37500
Skewness-5.743927508
Sum171199702.4
Variance1.104973314e+10
2021-03-27T17:02:26.355751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
150000 32 2.7%
 
125000 31 2.6%
 
175000 24 2.0%
 
187500 23 2.0%
 
100000 22 1.9%
 
115000 20 1.7%
 
112500 19 1.6%
 
137500 19 1.6%
 
91666.66667 19 1.6%
 
95000 18 1.5%
 
Other values (267) 948 80.7%
 
ValueCountFrequency (%) 
-1500000 1 0.1%
 
-850000 2 0.2%
 
-775000 1 0.1%
 
-745000 1 0.1%
 
-498000 1 0.1%
 
ValueCountFrequency (%) 
625000 2 0.2%
 
600000 1 0.1%
 
460000 1 0.1%
 
450000 2 0.2%
 
437500 1 0.1%
 

deltaAvgPrice
Real number (ℝ)

HIGH CORRELATION
Distinct count741
Unique (%)63.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22239.894770579634
Minimum-1794036.3636363638
Maximum418361.1111111111
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:26.441913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1794036.364
5-th percentile-257152.5258
Q1-32567.98623
median42361.11111
Q3111124.02
95-th percentile267361.1111
Maximum418361.1111
Range2212397.475
Interquartile range (IQR)143692.0062

Descriptive statistics

Standard deviation190988.8177
Coefficient of variation (CV)8.587667327
Kurtosis23.37589282
Mean22239.89477
Median Absolute Deviation (MAD)71776.03175
Skewness-3.303256755
Sum26131876.36
Variance3.647672848e+10
2021-03-27T17:02:26.523150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
69137.14286 8 0.7%
 
79137.14286 6 0.5%
 
114137.1429 6 0.5%
 
-11633.02752 6 0.5%
 
74137.14286 6 0.5%
 
-85701.92308 6 0.5%
 
64298.07692 6 0.5%
 
86670.45455 6 0.5%
 
49137.14286 6 0.5%
 
56284.40367 6 0.5%
 
Other values (731) 1113 94.7%
 
ValueCountFrequency (%) 
-1794036.364 2 0.2%
 
-1594036.364 1 0.1%
 
-1322050 1 0.1%
 
-1082638.889 1 0.1%
 
-1055862.857 1 0.1%
 
ValueCountFrequency (%) 
418361.1111 1 0.1%
 
415963.6364 1 0.1%
 
410963.6364 1 0.1%
 
396086.0215 1 0.1%
 
392361.1111 1 0.1%
 

deltaMedianPrice
Real number (ℝ)

HIGH CORRELATION
ZEROS
Distinct count304
Unique (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-24545.957446808512
Minimum-1997500.0
Maximum374000.0
Zeros51
Zeros (%)4.3%
Memory size9.3 KiB
2021-03-27T17:02:26.613465image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1997500
5-th percentile-333000
Q1-61250
median5000
Q370000
95-th percentile175750
Maximum374000
Range2371500
Interquartile range (IQR)131250

Descriptive statistics

Standard deviation194272.3067
Coefficient of variation (CV)-7.91463552
Kurtosis29.93036674
Mean-24545.95745
Median Absolute Deviation (MAD)65000
Skewness-4.094590967
Sum-28841500
Variance3.774172915e+10
2021-03-27T17:02:26.692466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 51 4.3%
 
5000 25 2.1%
 
50000 24 2.0%
 
10000 23 2.0%
 
-20000 21 1.8%
 
-15000 20 1.7%
 
55000 20 1.7%
 
70000 19 1.6%
 
15000 19 1.6%
 
75000 19 1.6%
 
Other values (294) 934 79.5%
 
ValueCountFrequency (%) 
-1997500 2 0.2%
 
-1797500 1 0.1%
 
-1405000 1 0.1%
 
-1170000 1 0.1%
 
-1115000 1 0.1%
 
ValueCountFrequency (%) 
374000 1 0.1%
 
331000 1 0.1%
 
324000 1 0.1%
 
305000 1 0.1%
 
295000 2 0.2%
 

dublinNorthSouth
Categorical

HIGH CORRELATION
Distinct count2
Unique (%)0.2%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
S
596
N
579
ValueCountFrequency (%) 
S 596 50.7%
 
N 579 49.3%
 
2021-03-27T17:02:26.796725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Length

Max length1
Mean length1
Min length1
ValueCountFrequency (%) 
Uppercase_Letter 2 100.0%
 
ValueCountFrequency (%) 
Latin 2 100.0%
 
ValueCountFrequency (%) 
ASCII 2 100.0%
 

distToCity
Real number (ℝ≥0)

Distinct count1152
Unique (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.068553655055304
Minimum0.09991156306907832
Maximum16.56325021500442
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:26.884617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.09991156307
5-th percentile1.388228897
Q13.204927425
median5.502717311
Q38.784903763
95-th percentile12.0909124
Maximum16.56325022
Range16.46333865
Interquartile range (IQR)5.579976338

Descriptive statistics

Standard deviation3.469708067
Coefficient of variation (CV)0.5717520622
Kurtosis-0.6625336301
Mean6.068553655
Median Absolute Deviation (MAD)2.763996005
Skewness0.4517911104
Sum7130.550545
Variance12.03887407
2021-03-27T17:02:26.970039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
8.438129316 3 0.3%
 
5.520706604 2 0.2%
 
5.229600299 2 0.2%
 
1.463954511 2 0.2%
 
2.213178012 2 0.2%
 
2.415355904 2 0.2%
 
8.239632795 2 0.2%
 
3.590435795 2 0.2%
 
10.21478363 2 0.2%
 
1.475217131 2 0.2%
 
Other values (1142) 1154 98.2%
 
ValueCountFrequency (%) 
0.09991156307 1 0.1%
 
0.1835478493 1 0.1%
 
0.2065863985 1 0.1%
 
0.3172734398 1 0.1%
 
0.346479514 1 0.1%
 
ValueCountFrequency (%) 
16.56325022 1 0.1%
 
16.24328956 1 0.1%
 
16.14727723 1 0.1%
 
15.94972834 1 0.1%
 
15.89190135 1 0.1%
 

daysSincePublished
Real number (ℝ≥0)

Distinct count157
Unique (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.49191489361702
Minimum1
Maximum367
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:27.056170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q130
median66
Q3107
95-th percentile142
Maximum367
Range366
Interquartile range (IQR)77

Descriptive statistics

Standard deviation46.11907477
Coefficient of variation (CV)0.6542463038
Kurtosis0.9919174926
Mean70.49191489
Median Absolute Deviation (MAD)37
Skewness0.6444218802
Sum82828
Variance2126.969057
2021-03-27T17:02:27.141012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
82 31 2.6%
 
66 24 2.0%
 
102 23 2.0%
 
24 23 2.0%
 
67 22 1.9%
 
115 21 1.8%
 
114 21 1.8%
 
43 19 1.6%
 
95 19 1.6%
 
22 19 1.6%
 
Other values (147) 953 81.1%
 
ValueCountFrequency (%) 
1 4 0.3%
 
2 6 0.5%
 
3 14 1.2%
 
4 16 1.4%
 
5 13 1.1%
 
ValueCountFrequency (%) 
367 1 0.1%
 
241 1 0.1%
 
238 2 0.2%
 
224 1 0.1%
 
220 1 0.1%
 

numFood
Real number (ℝ≥0)

Distinct count46
Unique (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.13106382978724
Minimum11
Maximum60
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:27.236374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile29
Q142
median47
Q350
95-th percentile53
Maximum60
Range49
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.409408367
Coefficient of variation (CV)0.1641753537
Kurtosis2.063909171
Mean45.13106383
Median Absolute Deviation (MAD)4
Skewness-1.399556958
Sum53029
Variance54.89933234
2021-03-27T17:02:27.317054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
49 128 10.9%
 
50 113 9.6%
 
51 89 7.6%
 
45 80 6.8%
 
48 77 6.6%
 
47 74 6.3%
 
52 65 5.5%
 
46 60 5.1%
 
44 49 4.2%
 
53 46 3.9%
 
Other values (36) 394 33.5%
 
ValueCountFrequency (%) 
11 1 0.1%
 
14 1 0.1%
 
15 1 0.1%
 
16 2 0.2%
 
18 1 0.1%
 
ValueCountFrequency (%) 
60 1 0.1%
 
59 1 0.1%
 
58 1 0.1%
 
57 1 0.1%
 
56 5 0.4%
 

numRecreation
Real number (ℝ≥0)

Distinct count23
Unique (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.897021276595744
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:27.404576image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q19
median12
Q315
95-th percentile18
Maximum24
Range23
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.994517066
Coefficient of variation (CV)0.3357577475
Kurtosis-0.1638100472
Mean11.89702128
Median Absolute Deviation (MAD)3
Skewness-0.328708574
Sum13979
Variance15.95616659
2021-03-27T17:02:27.484057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12 122 10.4%
 
13 121 10.3%
 
14 121 10.3%
 
11 111 9.4%
 
15 96 8.2%
 
16 84 7.1%
 
8 78 6.6%
 
10 77 6.6%
 
9 75 6.4%
 
17 63 5.4%
 
Other values (13) 227 19.3%
 
ValueCountFrequency (%) 
1 3 0.3%
 
2 7 0.6%
 
3 20 1.7%
 
4 46 3.9%
 
5 26 2.2%
 
ValueCountFrequency (%) 
24 2 0.2%
 
23 2 0.2%
 
21 3 0.3%
 
20 7 0.6%
 
19 20 1.7%
 

numShop
Real number (ℝ≥0)

Distinct count38
Unique (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.884255319148936
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Memory size9.3 KiB
2021-03-27T17:02:27.572773image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q318
95-th percentile27
Maximum38
Range37
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.763335986
Coefficient of variation (CV)0.6532454729
Kurtosis0.2006646826
Mean11.88425532
Median Absolute Deviation (MAD)3
Skewness1.029547918
Sum13964
Variance60.26938562
2021-03-27T17:02:27.655456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
6 311 26.5%
 
7 107 9.1%
 
5 64 5.4%
 
8 54 4.6%
 
11 46 3.9%
 
18 46 3.9%
 
17 43 3.7%
 
20 39 3.3%
 
4 35 3.0%
 
9 35 3.0%
 
Other values (28) 395 33.6%
 
ValueCountFrequency (%) 
1 9 0.8%
 
2 15 1.3%
 
3 14 1.2%
 
4 35 3.0%
 
5 64 5.4%
 
ValueCountFrequency (%) 
38 1 0.1%
 
37 1 0.1%
 
36 3 0.3%
 
35 5 0.4%
 
34 3 0.3%
 

Interactions

2021-03-27T17:01:57.953899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:58.073698image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:58.177341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:58.287725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:58.393361image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:58.501012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:58.616898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:58.717569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:58.820958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:58.925190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:59.029611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:59.142805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:59.253627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:59.357385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:59.462178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:59.570374image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:59.674892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:59.780933image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:01:59.895342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:00.132414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:00.245029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:00.360635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:00.461135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:00.565880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:00.670328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:00.773479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:00.882238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:00.993574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:01.097542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:01.202972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:01.310826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:01.423595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:01.534407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:01.651501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:01.768187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:01.883513image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:02.007755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:02.114939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:02.227314image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:02.338649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:02.451500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:02.565936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:02.685093image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:02.799879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:02.911889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:03.028655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:03.138302image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:03.245862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:03.356734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:03.464581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:03.573912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:03.691470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:03.794797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:03.902010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:04.013938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:04.120331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:04.231664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:04.344857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:04.578712image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:04.686473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:04.800621image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:04.912406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:05.022716image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:05.136639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:05.247244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:05.358403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:05.478002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:05.582178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:05.693768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:05.802333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:05.912359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:06.025643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:06.152265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:06.269940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:06.389171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:06.503668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:06.623642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:06.745549image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:06.871494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:06.994558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:07.118684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:07.249535image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:07.365746image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:07.485484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:07.612140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:07.733151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:07.856035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:07.983244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:08.102075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:08.222890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:08.348091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:08.448865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:08.548518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:08.652160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:08.753015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:08.854781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:08.964534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:09.060548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:09.162792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:09.263800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:09.364115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:09.467216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:09.572947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:09.670524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:09.769379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:09.875877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:10.186308image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:10.302810image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:10.413758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:10.520414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:10.627832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:10.743220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:10.843406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:10.946879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:11.051636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:11.154674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:11.265492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:11.376044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:11.479106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:11.581956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:11.690124image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:11.793282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:11.895880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:12.005521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:12.110609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-27T17:02:14.135299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-27T17:02:14.344049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:14.451711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:14.562770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:14.666786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:14.771473image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:14.882959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:14.993476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:15.100995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:15.217197image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:15.328482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:15.440279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:15.563003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:15.667304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:15.776590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:15.884875image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:15.995053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:16.107738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:16.231415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:16.349993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:16.468636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:16.591266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:16.705620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:17.071880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:17.203567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:17.319161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:17.437318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:17.566958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:17.679247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:17.794481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:17.908133image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:18.022774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-27T17:02:18.261715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-27T17:02:18.487909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:18.606143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:18.710681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:18.814804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:18.925885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:19.035984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-27T17:02:19.788299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:19.902594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-27T17:02:21.715812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:21.824702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:21.935754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:22.045474image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:22.164172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:22.278420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:22.397221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:22.522417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:22.632031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:22.753436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:22.869593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:22.984489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:23.103686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:23.227623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:23.343831image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:23.456856image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-27T17:02:27.780325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-27T17:02:28.021720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-27T17:02:28.235110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-27T17:02:28.458057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-27T17:02:28.669873image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-27T17:02:23.724504image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-27T17:02:24.129974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

df_indextitleneighbourhoodpropertyTypenumBedroomsnumBathroomsfloorAreapriceratingsellerIdlongitudelatitudepricePerBedroomdeltaAvgPricedeltaMedianPricedublinNorthSouthdistToCitydaysSincePublishednumFoodnumRecreationnumShop
00170 Mount Garrett Park, Tyrellstown, Tyrrelstown, Dublin 15Dublin 15Apartment2.02.065200000.0C11815-6.39290953.422384100000.000000144137.14285785000.0N11.799143932520
12Muir Cu, 6 Westwood Road, Finglas, Dublin 11Dublin 11End of Terrace3.01.095260000.0E21815-6.31998953.38412186666.66666731284.40367015000.0N5.3643791647162
24Apartment 35, Cloonlara Square, Phoenix Park Racecourse, Castleknock, Dublin 15Dublin 15Apartment2.02.075340000.0B34274-6.33765853.373575170000.0000004137.142857-55000.0N5.7515179401013
3126 Sycamore Avenue, Castleknock, Dublin 15Dublin 15Semi-D3.01.094390000.0D11590-6.38702553.375988130000.000000-45862.857143-105000.0N8.925828544923
4135 Ellensborough Court, Kiltipper Road, Kiltipper, Dublin 24Dublin 24Semi-D3.03.0124359000.0C12608-6.36659253.268978119666.666667-76081.395349-89000.0S11.8054361223420
514Apartment 5, Glenesky Square, Phoenix Park Racecourse, Castleknock, Dublin 15Dublin 15Apartment3.02.086340000.0C312-6.33787153.372638113333.3333334137.142857-55000.0N5.7242471401013
616677 Collins Avenue Extension, Whitehall, Dublin 9Dublin 9Semi-D3.02.0110395000.0D13658-6.26278253.388519131666.666667-11633.027523-20000.0N3.948689851107
74148 Pinewood Park, Rathfarnham, Dublin 14Dublin 14Semi-D3.01.0103550000.0E212149-6.29704653.289811183333.33333371086.02150549000.0S7.49535925461118
84482 St. James Road, Walkinstown, Dublin 12Dublin 12Terrace3.02.088365000.0D28647-6.33437853.313104121666.666667-47421.686747-70000.0S6.74110521391116
94711 Old Farm, Carpenterstown Road, Castleknock, Dublin 15Dublin 15Apartment1.02.051195000.0D27648-6.39315153.376222195000.000000149137.14285790000.0N9.3230001643923

Last rows

df_indextitleneighbourhoodpropertyTypenumBedroomsnumBathroomsfloorAreapriceratingsellerIdlongitudelatitudepricePerBedroomdeltaAvgPricedeltaMedianPricedublinNorthSouthdistToCitydaysSincePublishednumFoodnumRecreationnumShop
11652776Apartment 62, Fitzwilliam Quay Apartments, Ringsend, Dublin 4Dublin 4Apartment2.01.056320000.0D211062-6.22700453.340166160000.000000385963.636364182500.0S2.5087643649166
1166278410 Shamrock Cottages, North Strand, Dublin 3Dublin 3Townhouse2.01.049200000.0G2050-6.24414053.355253100000.000000277950.000000195000.0N0.9499145249136
11672791133 Kiltipper Gate, Tallaght, Dublin 24Dublin 24Apartment2.02.070215000.0ZZZ8210-6.37067253.269495107500.00000067918.60465155000.0S11.9279894323419
11682796Winterbourne, Cruagh Road, Rathfarnham, Dublin 14Dublin 14Detached4.02.0163595000.0E249-6.30884453.242353148750.00000026086.0215054000.0S12.76163591174
116927982 Florence Street, Portobello, Dublin 8Dublin 8Terrace3.02.0124595000.0E28456-6.26972353.331242198333.333333-255701.923077-297500.0S2.55045711753126
117028009 Saint Johns Court, Kilmore Road, Artane, Dublin 5Dublin 5Terrace2.01.071285000.0C31063-6.21893153.390851142500.000000117608.24742395000.0N4.9303235255149
11712809Apt 4, Slane House, Patrick Street, Christchurch, Dublin 8Dublin 8Apartment1.01.037230000.0E26223-6.27255253.340047230000.000000109298.07692367500.0S1.742339305386
117228154 Old Mount Pleasant, Ranelagh, Dublin 6Dublin 6Terrace3.01.0154825000.0SI_666262-6.25815853.326563275000.000000-207638.888889-295000.0S2.9489463153157
1173281710 Ard Na Greine, Eaton Brae, off Orwell Road, Rathgar, Dublin 6Dublin 6Terrace2.02.0144900000.0A39460-6.26159553.303673450000.000000-282638.888889-370000.0S5.4977947550179
117428183 Marian Drive, Rathfarnham, Dublin 14Dublin 14Detached5.03.0214850000.0C111062-6.29615753.295243170000.000000-228913.978495-251000.0S6.9102015450128